A Comprehensive Optical Search for Pre-explosion Outbursts from the Quiescent Progenitor of SN 2023ixf
Bibliographic record
Abstract
Abstract We perform a comprehensive search for optical precursor emission at the position of SN 2023ixf using data from the DLT40, ZTF, and ATLAS surveys. By comparing the current data set with precursor outburst hydrodynamical model light curves, we find that the probability of a significant outburst within 5 yr of explosion is low, and the circumstellar material (CSM) ejected during any possible precursor outburst is likely smaller than ∼0.015M ⊙. By comparing to a set of toy models, we find that, if there was a precursor outburst, the duration must have been shorter than ∼100 days for a typical brightness of M r ≃ −9 mag or shorter than 200 days for M r ≃ −8 mag; brighter, longer outbursts would have been discovered. Precursor activity like that observed in the normal Type II SN 2020tlf (M r ≃ −11.5) can be excluded in SN 2023ixf. If the dense CSM inferred by early flash spectroscopy and other studies is related to one or more precursor outbursts, then our observations indicate that any such outburst would have to be faint and only last for days to months, or it occurred more than 5 yr prior to the explosion. Alternatively, any dense, confined CSM may not be due to eruptive mass loss from a single red supergiant progenitor. Taken together, the results of SN 2023ixf and SN 2020tlf indicate that there may be more than one physical mechanism behind the dense CSM inferred around some normal Type II supernovae.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".